Applied Regression Analysis, Volume 1This book provides a standard, basic course in multiple linear regression, but it also includes material that either has not previously appeared in a textbook or, if it has appeared, is not generally available. |
Contents
CHAPTER PAGE | 1 |
THE MATRIX APPROACH TO LINEAR REGRESSION | 44 |
THE EXAMINATION OF RESIDUALS | 86 |
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analysis of variance b₁ B₁X b₂ calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits Constant Term correlation matrix Decoded B Coefficient degrees of freedom deviation of residuals estimation space example F value fitted equation freedom Determinant value independent variables lack of fit least squares estimate linear regression mean square method multiple regression nonlinear normal equations Observed Y Predicted obtained orthogonal orthogonal polynomials Overall F Total parameters Partial Correlation Coefficients plot polynomials Predicted Y Residual prediction equation procedure pure error regression analysis regression equation Residual Analysis Residual Normal Deviate residuals Mean response mean Degrees response Std Response variable Source df SS Source of Variation Square of Partials SS MS F ẞ₁ ẞo Standard deviation Statistics sum of squares Term in Prediction Total corrected Total Regression transformations variance table vector X₁ X₂ Y₁ Y₂ Z₁ zero σ²